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• W2287209969 abstract "We proposed a weighted l1 minimization to recover a sparse signal vector and the corrupted noise vector from a linear measurement when the sensing matrix A is an m by n row i.i.d subgaussian matrix. We obtain both uniform and nonuniform recovery guarantees when the corrupted observations occupy a constant fraction of the total measurement, provided that the signal vector is sparse enough. In the uniform recovery guarantee, the upper-bound of the cardinality of the signal vector required in this paper is asymptotically optimal. While in the non-uniform recovery guarantee, we allow the proportion of corrupted measurements grows arbitrarily close to 1, and the upper-bound of the cardinality of the signal vector is better than those in a recent literature [1] by a ln(n) factor." @default.
• W2287209969 created "2016-06-24" @default.
• W2287209969 creator A5037333528 @default.
• W2287209969 date "2016-01-22" @default.
• W2287209969 modified "2023-09-27" @default.
• W2287209969 title "Compressed sensing with corrupted observations" @default.
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• W2287209969 hasPublicationYear "2016" @default.
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